Tagged: Shelby Miller

The other day, I discussed predicting pitchers’ strikeout rates using xK%. I will conduct the same exercise today in regard to predicting walks. Using my best intuition, I want to see how well a pitcher’s walk rate (BB%) actually correlates with what his walk rate should be (expected BB%, henceforth “xBB%”). Similarly to xK%, I used my intuition to best identify reliable indicators of a pitcher’s true walk rate using readily available data.

An xBB% metric, like xK%, would not only if a pitcher perennially over-performs (or under-performs) his walk rate but also if he happened to do so on a given year. This article will conclude by looking at how the difference in actual and expected walk rates (BB – xBB%) varied between 2014 and career numbers, lending some insight into the (un)luckiness of each pitcher.

Courtesy of FanGraphs, I constructed another set of pitching data spanning 2010 through 2014. This time, I focused primarily on what I thought would correlate with walk rate: inability to pitch in the zone and inability to incur swings on pitches out of the zone. I also throw in first-pitch strike rate: I predict that counts that start with a ball are more likely to end in a walk than those that start with a strike. Because FanGraphs’ data measures ability rather than inability — “Zone%” measures how often a pitcher hits the zone; “O-Swing%” measures how often batters swing at pitches out of the zone; “F-Strike%” measures the rate of first-pitch strikes — each variable should have a negative coefficient attached to it.

I specify a handful of variations before deciding on a final version. Instead of using split-season data (that is, each pitcher’s individual seasons from 2010 to 2014) for qualified pitchers, I use aggregated statistics because the results better fit the data by a sizable margin. This surprised me because there were about half as many observations, but it’s also not surprising because each observation is, itself, a larger sample size than before.

At one point, I tried creating my own variable: looks (non-swings) at pitches out of the zone. I created a variable by finding the percentage of pitches out of the zone (1 – Zone%) and multiplied it by how often a batter refused to swing at them (1 – O-Swing%). This version of the model predicted a nice fit, but it was slightly worse than leaving the variables separated. Also, I ran separate-but-equal regressions for PITCHf/x data and FanGraphs’ own data. The PITCHf/x data appeared to be slightly more accurate, so I proceeded using them.

Again, R-squared indicates how well the model fits the data. An R-squared of .64 is not as exciting as the R-squared I got for xK%; it means the model predicts about 64 percent of the fit, and 36 percent is explained by things I haven’t included in the model. Certainly, more variables could help explain xBB%. I am already considering combining FanGraphs’ PITCHf/x data with some of Baseball Reference‘s data, which does a great job of keeping track of the number of 3-0 counts, four-pitch walks and so on.

And again, for the reader to use the equation above to his or her benefit, one would plug in the appropriate values for a player in a given season or time frame and determine his xBB%. Then one could compare the xBB% to single-season or career BB% to derive some kind of meaningful results. And (one more) again, I have already taken the liberty of doing this for you.

Instead of including every pitcher from the sample, I narrowed it down to only pitchers with at least three years’ worth of data in order to yield some kind of statistically significant results. (Note: a three-year sample is a small sample, but three individual samples of 160+ innings is large enough to produce some arguably robust results.) “Avg BB% – xBB%” (or “diff%”) takes the average of a pitcher’s difference between actual and expected walk rates from 2010 to 2014. It indicates how well (or poorly) he performs compared to his xBB%: the lower a number, the better. This time, I included “t-score”, which measures how reliable diff% is. The key value here is 1.96; anything greater than that means his diff% is reliable. (1.00 to 1.96 is somewhat reliable; anything less than 1.00 is very unreliable.) Again, this is slightly problematic because there are five observations (years) at most, but it’s the best and simplest usable indicator of simplicity.

Thus, Mark Buehrle, Mike Leake, Hiroki Kuroda, Doug Fister, Tim Hudson, Zack Greinke, Dan Haren and Bartolo Colon can all reasonably be expected to consistently out-perform their xBB% in any given year. Likewise, Aaron Harang, Colby Lewis, Ervin Santana and Mat Latos can all reasonably be expected to under-perform their xBB%. For everyone else, their diff% values don’t mean a whole lot. For example, R.A. Dickey‘s diff% of +0.03% doesn’t mean he’s more likely than someone else to pitch exactly as good as his xBB% predicts him to; in fact, his standard deviation (StdDev) of 0.93% indicates he’s less likely than just about anyone to do so. (What it really means is there is only a two-thirds chance his diff% will be between -0.90% and +0.96%.)

As with xK%, I compiled a list of fantasy-relevant starters with only two years’ worth of data that see sizable fluctuations between 2013 and 2014. Their data, at this point, is impossible (nay, ill-advised) to interpret now, but it is worth monitoring.

Miller is an interesting case: he was atrociously bad about gifting free passes in 2014, but his diff% was only marginally worse than it was in 2013. It’s possible that he was a smart buy-low for the braves — but it’s also possible that Miller not only perennially under-performs his xBB% but is also trending in the wrong direction.

I’m not gonna lie, I have no idea why Cobb, Corey Kluber and others show up as only having one year of data when they have two in the xK% dataset. This is something I noticed now. Their exclusion doesn’t fundamentally change the model’s fit whatsoever because it did not rely on split-season data; I’m just curious why it didn’t show up in FanGraphs’ leaderboards. Oh well.

Implications: Richards and Roark perhaps over-performed. Meanwhile, it’s possible that Odorizzi, Ross and Ventura will improve (or regress) compared to last year. I’m excited about all of that. Richards will probably be pretty over-valued on draft day.

The stove is hot, people. HOT! And as Every Time I Die once said: I been gone a long time. Sorry about that. I finished the first term of my last year of graduate school. It was probably the hardest one, and it should be smooth sailing from here on out.

I’m also pretty proud of a research paper I just completed regarding the probability of future success of minor leagues. The results are robust and I couldn’t be more pleased. It was a school project, so I didn’t have time to make it nearly as complex as I would have hoped, but it’s something I plan to further investigate in the coming days, weeks, months, what-have-you.

Anyway, there is plenty of news flying around as well as plenty of analysis. I’ll do my best to recap, but surely I’ll miss some things:

And I’m ignoring all the prospects involved as well. Marcus Semien, Austin Barnes, Jairo Diaz and others got shipped. I can only imagine a whole lot more action will be happening soon, as there still are teams with surpluses and deficits at all positions and some big-name free agents left on the market, including Max Scherzer and James Shields.

It is clear, however, that the Cubs and Blue Jays intend to more than simply contend. I would say the Marlins intend to as well, but I don’t even think they know what they’re doing, let alone we do. The White Sox are looking like a trendy sleeper with some key pitching additions (LaRoche is also an addition, but far from what I would call a “key” one), but they are far from a championship team.

But with so much more yet to happen, maybe it’s best to wait and see. There are obviously some ballpark and team-skill implications that will affect all these players’ projections, but I’ll get around to those in 2015.

I’ve finished my preliminary set of pitcher projections. I’ll share them but they’ll see some refining by the time March rolls around.

I’m also looking at how my projections fared last year. That will come in the next couple of days.

Keep your ear to the ground, people. Or to the stove. Never mind. Terrible idea. You’ll burn yourself. Just keep it to the ground.

This just in, folks: Corey Kluberleads all MLB pitchers in wins above replacement (WAR). The great thing about running your own website is you have full discretion to toot your own horn when you please. As much as I find it tacky to do so, I made bold predictions for a reason: to see if my projections are actually worth a damn. I just wish I had time to make more; I should have started early in the offseason as I ran out of time the longer the academic year has worn on. (I’m a graduate student, so publishing to this website is not always the most optimal use of my time. According to societal expectations, at least — I think it’s a great use of my time!)

Anyway, let’s revisit my bold predictions to discuss a) their accuracy thus far, and b) why they have (or have not) been accurate. Here they are, in chronological order:

Ross is ranked 31st of all starters, according to ESPN’s Player Rater. Instead of rehashing details, you can read the linked article to see why I glowed about Ross this offseason and have chosen him as a streamer several times already this year (before he gained more recognition and, consequently, more ownership). That he qualifies as a reliever in ESPN leagues is a huge plus as well. I readily admit it’s not insane for a random names to rank highly in the player rater; just check out the names around Ross’, including Alfredo Simon, Josh Beckett, Aaron Harang and Collin McHugh. Unlike the names I mentioned, though, I think Ross has the natural ability to stay there, given his strikeout propensity that limit the damage done by walks (which, by the way, is a problem nowhere near as bad as Shelby Miller‘s — I guess six wins will mask his atrociously bad WHIP that will blow up in his face sooner rather than later.) Ross is still available in 21 percent of ESPN leagues, so if he’s out there, you should grab him. Just don’t expect him to keep winning as often in front of that terrible San Diego offense.

As terribly as this prediction has turned out — Miller is batting .151/.230/.247 with 3 HR and 3 SB — I do not regret making it. Miller has struck out in 28 percent of his plate appearances, which is way, way worse than he ever was in Triple-A or even last year, when he struck out 17 percent of the time. It pains me deeply that The Triple Machine hasn’t hit a triple. Have I given up on him this year? Honestly, yes. His batting average on balls in play is grossly unlucky right now, but even regression to the mean won’t fix what his strikeout tendency has broken. But I still like him as a sleeper for next year, or even as a late bloomer this year. If he can demonstrate an improvement in his plate discipline as the year wears on, I will give him another chance. It upsets me, though, that he had such a hot spring. It fuels the fire of analysts who criticize spring training stats as unreliable. I agree, to an extent, but Miller’s spring stats were an extension of his 2013 season — albeit an extension inflated by some good luck. It’s worth emphasizing here that strikeouts really aren’t luck-based, so to say the his spring training was lucky is an ignorant dismissal.

There’s one thing I, at least, can privately appreciate about my bold predictions: I abided by all of them in every single I’m in, unless someone happened to grab a pitcher before me. Ultimately, in four leagues, I grabbed Ross and Miller in four of them, and Kluber in three — and in the fourth one, I promptly traded Jayson Werth and Tyson Ross (who I drafted in the last round) for Norichika Aoki and Corey Kluber (this is a points league, so Aoki carries some value for his lack of K’s and contact approach). Did I win the trade? Who knows — I traded one guy I liked for another I liked more. Point is, I actually rolled with my bold predictions. Might as well eat my words, right? (Is that how that saying goes?)

I got Kluber in the equivalent of the last round in every draft and for $1 in my primary keeper auction league. Yes, I’m bragging. But, more importantly, this isn’t a revelation to me. I knew Kluber would be good based on last year’s peripherals, as did a host of other people on FanGraphs (namely, Carson Cistulli and the Corey Kluber Society). But a lot of people didn’t see it coming, which is crazy to me, and it makes me question what it really takes to become a paid professional “fantasy expert.” Tristan H. Cockcroft ranked Kluber 58th of starting pitchers this preseason, which is better than I expected, but look at some of the names above him: Matt Garza? Justin Masterson? Zack Wheeler? For a guy who invests so much in seeing an improvement in skills, Wheeler has been, for his entire career, buying up billboards to plaster them with slogans such as I HAVE CONTROL ISSUES. Kluber is essentially the antithesis of Wheeler. And, yet, who has the smaller track record? Ridiculous… (In Eric Karabell’s defense, he said pitching is so deep this year that owners may not be able to draft Kluber, which was a roundabout way of indicating he liked him, at least somewhat, heading into draft day.)

Anyway, I’m clearly on a rant, and I need to get this train back on the rails. Kluber is somehow not 100-percent owned at this point — he’s 99.9-percent owned, but hey, at least I’m not lying — yet he’s striking out everyone and their mothers. I don’t know if he continues to strike out 10 per nine innings (10.28 K/9), but the percentage of swinging strikes he has produced has jumped 1.4 percent, placing in the top 1o in the category, behind Max Scherzer and ahead of Madison Bumgarner. This is all a long-winded way of saying he could, and perhaps should, be a 200-K guy this year. In that sense, maybe he’s not a buy-low guy, but his lack of name recognition and his .350 BABIP makes him a prime candidate to be exactly that. A handful of rankings have him in the 35-to-40 range; even then, I can give you a case to trade perhaps a dozen names ahead of him for Kluber, including Gio Gonzalez, Matt Cain and, yes, maybe even Justin Verlander (who, at this point, is still owned in most leagues simply because of name recognition and past performance; and while I understand the importance of past performance, do not let yourself be blinded by nostalgia).

This one is random, but hey, it’s legit: Haren has only a 6.89 K/9 right now. You can read the linked post to find out way. I may rip him a little too hard — his control still makes him a fairly solid starter — but he’s more of a Kyle Lohse these days than, well, a Corey Kluber. Lohse is serviceable, but he’s not elite, and Haren should be able to net you an extra win or two along the way in front of a lethal Dodgers offense.

OK, that’s it. I’m 3-for-4 in my bold predictions so far this year, which is a pretty good day at the plate, so I’ll take it.

Also, the academic year is winding down, and once it winds down completely, Need a Streamer will ramp up with more content. Stay tuned, and thanks for reading.

What I’m saying is I’ve spent more time than I’d care to admit frolicking in free agency, trying to figure out which early-season studs are legit or not. I’ve been pondering various buy-low situations as well. So I jumped into a pool of peripherals and PITCHf/x data to look for answers.

The list below is not remotely exhaustive. It’s mostly players I am watching or already using as replacements for my teams. Here they are, in no particular order.

Jake Peavy, BOS | 0-0, 3.33 ERA, 1.48 WHIP, 9.25 K/9
Peavy’s prime came and went about five years ago, so, full disclosure, I don’t know as much about him off the top of my head as I should. But I do know one thing: he doesn’t strike out a batter per inning anymore. In his defense, batters’ contact rate against him is the best it has been since 2009, his last truly good year. So maybe he will strike out a few more batters than last year, but I think it’ll be closer to 2012’s 7.97 K/9, not 2009’s 9.74 K/9. The WHIP is atrocious; the walk rate is through the roof. If there’s a guy in your league who will pay for what will end up being the illusion of ERA and strikeouts, by all means, trade him. He’s owned in 100 percent of leagues but doesn’t deserve to be.Verdict: Sell high

John Lackey, BOS | 2-2, 5.25 ERA, 1.46 WHIP, 8.63 K/9Another Boston pitcher, another bad start to the season. I like Lackey a lot more, though, for a variety of reasons. One, last year’s renaissance was legitimate. Two, he’s not walking many batters right now, so his unspectacular ratios are more a result of an unlucky batting average on balls in play (.333 BABIP) than incompetence. Three, his swinging strike and contact rates are currently career bests. Again, we’re working with small sample sizes here, and this could easily regress. But considering his velocity is also at a career high, I don’t find it improbable that Lackey actually does better than he did last season. If an owner in your league has already dropped him, put in your waiver claim now.Verdict: Buy low

Jesse Chavez, OAK | 1-0, 1.38 ERA, 0.92 WHIP, 9.69 K/9Talk about unexpected. Chavez, who has been relevant about zero times, is making for an intriguing play in all leagues. It’s a given he will regress, especially considering the .242 BABIP, but his improved walk rate could be here to stay, as he is pounding the zone more than he ever has in his career. The strikeouts are somewhat of a mirage, but it looks like he can be a low-WHIP, moderate-strikeout guy, and that’s still valuable.Verdict: Sell really high, or just ride the hot hand

Nathan Eovaldi, MIA | 1-1, 3.55 ERA, 1.14 WHIP, 8.17 K/9I wouldn’t call Eovaldi a trendy sleeper, but he certainly was a sleeper coming into 2014. It was all about whether he could command his pitchers better — and, like magic, it appears he has, walking only 1.07 batters per nine innings as opposed to 3.39-per-nine last year. The swinging strike and contact rates are concerning, as they are the lowest of his career, so it’s hard to see his strikeout rate going anywhere but down. However, he’s throwing 65 percent of his pitches in the strike zone, highest of all qualified pitchers. So there are two ways to look at this. His control has probably legitimate improved. Unfortunately, even the masterful Cliff Lee only threw 53.3 percent of pitches in the zone last year, and I am hesitant to claim Eovaldi has better control than Lee. This could be a “breakout” year of sorts for Eovaldi, but I’m using that term liberally here. He’s only owned in 20.5 percent of leagues, so this makes him more of a ride-the-hot-hand type, like Mr. Chavez above.Verdict: Eventually drop, ideally before he does damage to your team

Mark Buehrle, TOR | 4-0, 0.64 ERA, 0.93 WHIP, 6.11 K/9Look, I have had a long-standing man crush on Buehrle, but this is ridiculous. You know better than I that these happy dreams will soon become nightmares, not because Buehrle is awful or anything, but because regression rears its head in occasionally very brutal ways.Verdict: Sell high

Alfredo Simon, CIN | 0.86 ERA, 0.81 WHIP, 5.57 K/9Something isn’t right here. A 0.81 WHIP and… fewer than six strikeouts per nine innings? As you become more familiar with sabermetrics, you quickly realize certain things don’t mesh. A low WHIP combined with the low strikeout rate is one of those things. I can tell you without looking that his BABIP is impossibly low — and, now looking, I see I’m right: it’s .197. Tristan H. Cockcroft of ESPN is all about Simon, and in his defense, Simon’s PITCHf/x data foreshadows some positive regression coming his way in the strikeout department. But it can only get worse from here for Simon. However, I think he has a bit of a Dan Straily look to him, and that’s certainly serviceable.Verdict: Sell high, or just ride the hot hand

Yovani Gallardo, MIL | 1.46 ERA, 1.09 WHIP, 6.93 K/9This is a disaster waiting to happen. Like Simon, his strikeout rate is low, but for Gallardo, it is deservedly so: his swinging strike and contact rates are, by far, career worsts. Meanwhile, his ratios are buoyed by a .264 BABIP and 89.8% LOB% (left-on-base percentage), despite his 74.7% career LOB%. The Brewers will fall with him. Sell high, and sell fast.Verdict: Sell high

Shelby Miller, STL | 3.57 ERA, 1.50 WHIP, 8.34 K/9Miller is the first pitcher on this list in whom owners actually invested a lot. Be patient. The 98.3-percent of owners who didn’t cut bait before his last start were surely rewarded. I imagine he’s leaving his pitches up in the zone, given his increased percentage of pitches thrown in the zone coupled with his home run rate. Speaking of which, he shouldn’t be walking five batters per nine innings when he’s throwing more than 50 percent of his pitches in the zone. He’ll be fine.Verdict: Buy low

Drew Hutchison, TOR | 3.60 ERA, 1.45 WHIP, 10.80 K/9I’ll be honest, I was surprised to see Hutchison’s xFIP stand at 3.43. It seems like he has been much worse — but has he really? The walks are problematic but not unmanageable (see: Matt Moore), and they’ve actually shored up a bit in his last couple of starts. Moreover, he is still striking out batters at an elite rate, and the PITCHf/x data supports his success, albeit probably not with quite as much success as he’s having now. As for the WHIP? A .365 BABIP sure doesn’t help. Hutchison was once a highly-touted prospect. Your window of opportunity to gamble on this live arm may be closing if he can keep his ERA down.Verdict: Add via free agency, sooner rather than later

As much as it feels good to correctly bet on a bounceback, it sucks harder to be the guy who loses the coin flip. I looked at my 2012 standard 5×5 rotisserie auction draft and the list is, frankly, hilarious. The top 10 pitchers were:

Wow. That was only two years ago. Half those names have fallen from grace — more than half if you’re in the camp that think last year was not an anomaly for Verlander and that we’ve reached the beginning of the end with him. It’s truly hard to believe that anyone thought Halladay would be the second-best pitcher in the MLB in 2012 after the numbers he put up, but it just goes to show how suddenly a pitcher’s decline can sneak up on everyone.

Humorously enough, three of the pitchers in that top 10 make my forthcoming list of pitchers who I will not be targeting in drafts. This can also be viewed as a list of the largest differences between ESPN’s and my rankings.

Justin Verlander | ESPN rank: 14, My rank: 25
I have more faith in his strikeout rate, but ESPN has more faith in his overall effectiveness. Truth is, he didn’t suffer an abnormally high BAbip or anything like that. He was simply more hittable and, honestly, ESPN’s projection doesn’t make a lot of sense when you consider that fewer strikeouts should lead to a higher probability he will give up a hit. Regardless of how you feel about him, it’s the offseason surgery that freaks me out. Does that not freak YOU out? It came out of nowhere, and there are rumors he may not even be ready for Opening Day. Toss in the fact that he has a pretty rigorous offseason routine that, for the first time, he won’t be able to stick to, and you have a guy that may not only start the season but also be out of shape, relative to his standards. Unless I get him as low as 30th, he’s not worth the risk.

Shelby Miller | ESPN rank: 26, My rank: 48
This is not a testament to Miller’s abilities — he’s a very good pitcher. This time, ESPN believes more in the strikeout rate; my research leads me to bet against it, although I’m sure he has the capability to improve. The most important aspect of his game this year will be how deeply he pitches into games. I’m not banking on 200 innings, let’s put it that way. I simply believe he will be overvalued on draft day, especially if ESPN thinks he will be better than Gerrit Cole or Alex Cobb. Even if Cole doesn’t ramp up the strikeouts, I still can’t get behind them on this one (Cole struck out 10 batters per nine innings over his handful of starts and was an absolute beast. He gasses 100 mph). Miller is o-ver-ra-ted. Case closed.

Hyun-jin Ryu | ESPN rank: 31, My rank: 50
I actually think he will perform better than ESPN thinks. I also think ESPN simply underrates a lot of players. They have an audience to please, and I think intuition prevails sometimes, even if it’s wrong. Ryu is good but not elite; he pitches more to contact but keeps the ball on the ground. With that said, the strikeout rate suffers, so he’s not really a guy I want on my team. However, he’ll get wins, and that’s great. But we all knows wins are unpredictable. Ask 2012 Cliff Lee and 2013 Cole Hamels. (Or maybe just don’t pitch for the Phillies next time.) Anyway, again, another case of overrating in my opinion.

Jon Lester | ESPN rank: 37, My rank: 56
With so much pitching depth, there’s no reason to tolerate a career 1.30 WHIP and a pedestrian K/9 rate since 2012 just to bank on wins. It only takes one bad year.

CC Sabathia | ESPN rank: 39, My rank: 41
At least ESPN and I are on the same page on this one. Still, what if it gets worse? I think 41st is a neutral projection, and with Hiroki Kuroda and Tony Cingrani following right behind, there are clearly other worthy commodities for which you can pass up Sabathia. Also, don’t forget that these rankings don’t tell you exactly how closely players are ranked together. Players within five slots or so of one another are practically interchangeable.

Dan Haren | ESPN rank: 44, My rank: 73
Let me make my official declaration: Dan Haren’s strikeout rate is NOT back — I repeat, NOT back! ESPN only sees a slight regression, but I dug deeper into PITCHf/x data and basically revealed Haren’s strikeout rate in 2013 was anomalous. I truly think he is more likely to record fewer than seven strikeouts per nine (aka 6.9 K/9) than 7.7 K/9 as expected by ESPN. Be warned, friends. The Dodgers will make his win column tolerable, but only if he pitches somewhat respectably — and I don’t know if he’s capable of doing that. As I’ve said a hundred times already, there’s simply too much volatility here.

Honorable Mentions:Julio Teheran – He’s good, but I’d rather another owner jump the gun on him (which I can almost guarantee will happen) and pass up on better talent for him.Jeff Samardzija – Serious question: has he ever won more than nine games? (Also, not coincidentally, a rhetorical question.)Zack Wheeler – ESPN is really bullish on him. Maybe I’ll be the guy who misses the breakout year, but he finished 2013 with a 4.1 BB/9. He walked 5+ guys in four starts, and failed to strike out more batters than he walked in five. That’s simply unacceptable, and command does not shore up overnight.

If FanGraphs were a home, or a hotel, or even a tent, I’d live there. I would swim in its oceans of data, lounge in its pools of metrics.

It houses a slew of PITCHf/x data — the numbers collected by the systems installed in all MLB ballparks that measure the frequency, velocity and movement of every pitch by every pitcher. It’s pretty astounding, but it’s also difficult for the untrainted eye to make something of the numbers aside from tracking the declining velocities of CC Sabathia‘s and Yovani Gallardo‘s fastballs.

I used linear regression to see how a pitcher’s contact, swinging strike and other measurable rates affect his strikeout percentage, and how that translates to strikeouts per inning (K/9). Ultimately, the model spits out a formula to generate an expected K/9 for a pitcher. I pulled data from FanGraphs comprised of all qualified pitchers from the last four years (2010 through 2013).

The idea is this: A pitcher who can miss more bats will strike out more batters. FanGraphs’ “Contact %” statistic illustrates this, where a lower contact rate is better. Similarly, a pitcher who can generate more swinging strikes (“SwStr %”) is more likely to strike out batters.

Using this theory coupled with the aforementioned data, I “corrected” the K/9 rates of all 2013 pitchers who notched at least 100 innings. Instead of detailing the full results, here are the largest differentials between expected and actual K/9 rates. (I will list only pitchers I deem fantasy relevant.)

There’s a lot to digest here, so I’ll break it down. It appears Perez was the unluckiest pitcher last year, of the ones who qualified for the study, notching almost 1.7 fewer strikeouts per nine innings than he would be expected to, given the rate of whiffs he induced. Conversely, rookie sensation Cingrani notched almost 2.2 more strikeouts per nine innings than expected.

There is a caveat. I was not able to account for facets of pitching such as a pitcher’s ability to hide the ball well, or his tendency to draw strikes-looking. With that said, a majority of the so-called lucky ones are pitchers who, in 2013, experienced a breakout (Cingrani, Fernandez, Miller, Darvish, Masterson, Tillman) or a renaissance (Jimenez, Kazmir, Masterson — woah, all Cleveland pitchers). Is it possible these pitchers can all repeat their performances — especially the ones who have disappointed us for years? Perhaps not.

(Update, Jan. 24: Cliff Lee’s mark of -1.86 is, amazingly, not unusual for him. Over the last four years, the average difference between his expected and actual K/9 rates is … drum roll … -1.88. Insane!)

Darvish and Liriano were in a league of their own in terms of inducing swings and misses, notching almost 30 percent each. (Anibal Sanchez was third-best with 27 percent. The average is about 21 percent.) However, Darvish recorded 2.78 more K/9 than Liriano. Is there any rhyme or reason to that? Darvish is, without much argument, the better pitcher — but is he that much better? I don’t think so. Darvish was expected to notch 10.41 K/9 given his contact rate. Any idea what his 2012 K/9 rate was? Incredibly: 10.40 K/9.

More big names produced equally interesting results. King Felix Hernandez recorded a career-best 9.51 K/9, but he was expected to produce something closer to 8.57 K/9. His rate the previous three years? 8.52 K/9.

Dan Haren didn’t produce much in the way of ERA in 2013, but he did see a much-needed spike in his strikeout rate, jumping above 8 K/9 for the first time since 2010. His expected 7.07 K/9 says otherwise, though, and it fits perfectly with how his K/9 rate was trending: 7.25 K/9 in 2011, 7.23 K/9 in 2012.

I think my models tend to exaggerate the more extreme results (most of which are noted in the lists above) because they could not account for intangibles in a player’s natural talent. However, they could prove to be excellent indicators of who’s due for regression.

Only time will tell. Maybe Jose Fernandez isn’t the elite pitcher we already think he is — not yet, at least.

————

Notes: The data almost replicates a normal distribution, with 98 of the 145 observations (67.6 percent) falling within one standard deviation (1.09 K/9) of the mean value (7.19 K/9), and 140 of 145 (96.6 percent) falling within two standard deviations. The median value is 7.27 K/9, indicating the distribution is very slightly skewed left.

I apologize for being that guy that ruins that ooey gooey feeling you get when think about the fantasy league you won last year. As much as you want to think you are a fantasy master — perhaps even a fantasy god — you should acknowledge that you probably benefited from a good deal of luck. Sure, for your sake, I will admit you made a great pick with Max Scherzer in the fifth round. But did you, in all your mastery, predict he would win 21 games?

Don’t say yes. You didn’t. And frankly, you would be crazy to say he’ll do it again.

I focus primarily on pitching in this blog, and let it be known that pitchers are not exempt from luck in the realm of fantasy baseball. If you’re playing in a standard rotisserie league, you probably have a wins category. In a points league, you likely award points for wins.

Wins. Arguably the most arbitrary statistic in baseball. Let’s not have that discussion, though, and instead simply accept the win as it is. The win has the most drastic uncontrollable effect on a fantasy pitcher’s value. (ERA and WHIP experiences similar statistical fluctuations, but at least they aren’t arbitrary.)

I had an idea, but before I proceed, let me interject: if you’re drafting for wins, you’re doing it wrong. But, as I said, you can’t ignore wins.

But let’s say you did, and drafted strictly on talent, or “stuff” (which, here, factors in a pitcher’s durability). How would the top 30 pitchers change? Here’s my “stuff” list, which you can compare with the base projections:

Here are the five players with the biggest positive change and a breakdown of each:

Brandon Beachy, up 23 spots
His injury history has weakened his wins column projection. Consequently, the number of innings Beachy is expected to throw is significantly less than a full season. But if he managed to stay healthy for the full year (say, 200 innings)? He’s a top-1o pick based on pure stuff. If you draft with the philosophy that you can always find a viable replacement on waivers, Beachy could be your big sleeper.

Marco Estrada, up 22 spots
Estrada’s diminished expected wins is more a function of his terrible team than ability. Estrada has underperformed the past two years, Ricky Nolasco style, but if he can pull it together, he’s a top-30 pitcher based on “stuff.” And hey, maybe he can luck into some extra wins. However, if he can’t pull it together — Ricky Nolasco style — he’ll be relegated to fringe starter.

Danny Salazar, up 9 spots
Salazar has immense potential. His injury history led the Indians to cap his per-game pitch count last year, and that has been factored into his projection. But if he’s a full-time, 200-inning starter? He’s a top-25 starter with top-15 upside. Again, this is in terms of “stuff”. But is Ivan Nova better than Felix Hernandez because he can magically win more games? Of course not. Among a slew of young studs, including Jose Fernandez, Shelby Miller, Michael Wacha and so on, Salazar is a diamond in the rough.

A.J. Burnett, up 8 spots
His projection is already plenty good. But you saw how many games he won in 2013. Anything can happen.

Corey Kluber, up 8 spots
Most people were probably scratching their heads when they saw Kluber’s name listed above. Frankly, I’m in love with him, and it’s because he’s a stud with a great K/BB ratio. I understand why someone may be inclined to dismiss it as an aberration, but his swinging strike and contact rates are truly excellent. Even if they regress, he should be a draft-day target.

Here are the three starting pitchers with the biggest negative change.

Anibal Sanchez, down 10 spots
He’s great, but he also plays for a great team. Call it Max Scherzer syndrome. He carries as big a risk as any other player to pitch great but only win five or six games, as do the next two players.

Hisashi Iwakuma, down 6 spots

Zack Greinke, down 4 spots

Let me be clear that although I created a hypothetical scenario where wins didn’t exist, I don’t advocate for blindly drafting based on “stuff.” It’s important to acknowledge that certain players have a much better chance to win than others. Chris Sale of the Chicago White Sox could win 17 games just as easily as he could win seven. It’s about playing the odds — and unless a pitcher truly pitches terribly, don’t blame the so-called experts for your bad luck. He probably put his money where his mouth is, too, and is suffering along with you.

Here is a more comprehensive list of pitchers ranked by “stuff,” if that’s the way you sculpt your strategy: